Predicting pathogenic DNA damage repair gene mutations in prostate cancer patients: a multi-center magnetic resonance imaging radiomics study.

IF 2.3 2区 医学 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Yuntian Chen, Jinge Zhao, Lei Ye, Diwei Zhao, Sha Zhu, Bangwei Fang, Fengnian Zhao, Ling Yang, Zhenhua Liu, Jindong Dai, Nanwei Xu, Yanfeng Tang, Haolin Liu, Zhipeng Wang, Xiang Tu, Fangjian Zhou, Qiang Wei, Dingwei Ye, Bin Song, Yonghong Li, Yao Zhu, Pengfei Shen, Hao Zeng, Jin Yao, Guangxi Sun
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引用次数: 0

Abstract

Background: Genetic testing for pathogenic DNA damage repair gene (pDDRg) mutations has clinical benefits for prostate cancer (PCa) patients, but its real-world application faces challenges due to its high associated costs. We sought to develop a magnetic resonance imaging (MRI)-based radiomics model capable of assessing the likelihood of PCa patients harboring pDDRg mutations. We then rigorously validated its predictive value in two external validation cohorts.

Methods: A total of 225 patients with both multiparametric MRI data before prostate biopsy and genetic testing information for pDDRg mutations were included in this study. The radiomics features were extracted from the T2-weighted imaging (T2WI) and apparent diffusion coefficient (ADC) sequences of the MRI images in the training cohort (N=101) using the least absolute shrinkage and selection operator (LASSO) algorithm. The area under the curve (AUC) values of the receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) were used to validate the predictive value of the model in both the internal (N=41) and external (N=83) validation cohorts.

Results: In total, 48 of the 225 (21.3%) patients in our cohort were identified by genetic testing as having positive pDDRg mutations, including BRCA1/2 (N=13), CDK12 (N=15), ATM (N=9), and other pDDRg mutations (N=17). Thirteen radiomics features from T2WI (N=7) and ADC sequences (N=6) were extracted to develop a model predicting pDDRg mutation carriers. The radiomics-based model had AUC values of 0.824 [95% confidence interval (CI): 0.677-0.923] in the internal validation dataset and 0.836 (95% CI: 0.738-0.908) in the external validation dataset. Notably, setting the cut-off value as "zero misseddignoses" resulted in a potential reduction of around 25% in unnecessary gene testing across both the internal and external validation datasets.

Conclusions: Our MRI radiomics-based predictive model is a promising pre-testing tool for pDDRg mutation prediction in patients with PCa. Prospective studies need to be conducted to further validate the power of this predictive model before its clinical application.

预测前列腺癌患者致病性DNA损伤修复基因突变:一项多中心磁共振成像放射组学研究。
背景:致病性DNA损伤修复基因(pDDRg)突变的基因检测对前列腺癌(PCa)患者具有临床益处,但由于其高昂的相关成本,其在现实世界中的应用面临挑战。我们试图开发一种基于磁共振成像(MRI)的放射组学模型,能够评估PCa患者携带pDDRg突变的可能性。然后,我们在两个外部验证队列中严格验证了其预测值。方法:225例同时具有前列腺活检前多参数MRI数据和pDDRg突变基因检测信息的患者纳入本研究。使用最小绝对收缩和选择算子(LASSO)算法从训练队列(N=101)的MRI图像的t2加权成像(T2WI)和表观扩散系数(ADC)序列中提取放射组学特征。采用受试者工作特征(ROC)曲线下面积(AUC)值和决策曲线分析(DCA)来验证模型在内部(N=41)和外部(N=83)验证队列中的预测价值。结果:225例患者中,共有48例(21.3%)患者通过基因检测发现pDDRg突变阳性,包括BRCA1/2 (N=13)、CDK12 (N=15)、ATM (N=9)和其他pDDRg突变(N=17)。从T2WI (N=7)和ADC序列(N=6)中提取13个放射组学特征,建立预测pDDRg突变携带者的模型。基于放射组学的模型在内部验证数据集中的AUC值为0.824[95%置信区间(CI): 0.677-0.923],在外部验证数据集中的AUC值为0.836 (95% CI: 0.738-0.908)。值得注意的是,将临界值设置为“零漏诊”,可以在内部和外部验证数据集中减少大约25%的不必要的基因测试。结论:我们基于MRI放射组学的预测模型是预测PCa患者pDDRg突变的一种很有前景的预测试工具。在临床应用之前,需要进行前瞻性研究以进一步验证该预测模型的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Quantitative Imaging in Medicine and Surgery
Quantitative Imaging in Medicine and Surgery Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
4.20
自引率
17.90%
发文量
252
期刊介绍: Information not localized
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